system

Abstract

We provide the system. [Solution] A server means that receives data from the collection means, An analysis means for analyzing the received data and detecting anomalies, A planning means for determining crime prevention actions based on the aforementioned abnormality, A terminal means for executing the aforementioned determined crime prevention action, A notification means for notifying the user of the execution result of the terminal means, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including 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] There is a concern that it is impossible to check in real time the situation of homes with insufficient security measures, the elderly or children living far away. Also, it is difficult to take appropriate actions promptly in case of emergency. In particular, in dual-income households, measures for ensuring the safety of family members are required.

Means for Solving the Problems

[0005] The present invention receives data collected from a plurality of sensors at a server and analyzes it using AI to detect abnormalities in order to strengthen security and monitoring. Then, it plans security actions based on the detected abnormalities and executes them on a terminal. Furthermore, by notifying the user of the execution results, it provides a system that can grasp the safety of family members in real time.

[0006] "Data collection means" refers to a device or process for acquiring data from multiple sensors installed within a home.

[0007] A "server system" is a core system that processes data received from collection systems and has analysis and planning functions.

[0008] "Analysis means" refers to the process of analyzing received data using AI or other methods to detect patterns that are different from the norm.

[0009] An "abnormality" refers to a data pattern or indication that shows a situation different from the norm within the home, and represents an unexpected event.

[0010] "Planning procedures" refer to the process of determining the optimal security action in response to detected anomalies.

[0011] A "terminal device" is a device used to execute a predetermined security action and controls various devices installed within the home.

[0012] "Execution result" refers to the outcome or status of an action performed by a terminal device.

[0013] A "notification method" is a process for informing the user of the execution results and providing information to the user's terminal.

[0014] A "user" refers to an individual or family member who manages this system and receives notifications. [Brief explanation of the drawing]

[0015] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0019] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] To implement this invention, multiple sensors installed in the home must collect data in real time. This involves a variety of sensors, including WiFi sensors, temperature sensors, humidity sensors, sound sensors, and cameras. These sensors continuously monitor the conditions within the home and transmit the data to a server at specified time intervals.

[0037] The server is responsible for analyzing the received data. AI algorithms are used for data analysis, recognizing anomalies by detecting patterns that deviate from normal conditions. For example, it recognizes suspicious movement during periods of low activity, sudden temperature changes, or unusual sounds as anomalies.

[0038] When an anomaly is detected, the server plans the optimal countermeasures based on a pre-configured security plan. This includes specific actions to enhance security. The planned actions are executed by various devices located within the home.

[0039] The device performs appropriate security actions based on instructions from the server. This includes automatically turning on lights in areas where an anomaly is detected and activating security alarms. It also ensures family safety by automatically locking digital locks or sending emergency calls depending on the level of risk detected by the anomaly.

[0040] Finally, users can receive detailed information about anomalies in real time through the application. Notifications include the type of anomaly, its location, and the actions taken, allowing users to monitor the situation at home via their smartphone or computer. After receiving a notification, users can also send further instructions to the system as needed, enabling them to respond flexibly to unexpected situations.

[0041] In this way, the present invention achieves highly accurate home security and monitoring through multiple sensors, AI analysis, and automated terminal control.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects data from multiple sensors installed in the home. These include sensors for temperature, humidity, sound, and cameras, and the data is measured at regular intervals.

[0045] Step 2:

[0046] The device sends the collected data to the server. This data includes numerical values ​​and image data based on the timestamp and sensor type.

[0047] Step 3:

[0048] The server analyzes the received data and compares it to normal patterns using an AI algorithm. This identifies data points that are considered anomaly.

[0049] Step 4:

[0050] When an anomaly is detected, the server determines the optimal security action based on a pre-configured security plan. The planned measures include risk assessment.

[0051] Step 5:

[0052] The terminal receives instructions from the server and executes planned security actions. These actions include specific measures such as turning on lights, activating security alarms, and locking digital locks.

[0053] Step 6:

[0054] Users receive notifications of anomaly detection through the application. The notifications include details of the location, nature of the anomaly, and the actions taken.

[0055] Step 7:

[0056] Users can use the application to send additional instructions to the system, enabling flexible responses to changing situations.

[0057] (Example 1)

[0058] 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."

[0059] In today's world, ensuring the safety of homes and facilities with high accuracy requires improving the efficiency and reliability of anomaly detection systems. Conventional systems can miss anomalies or, conversely, misidentify them, which reduces the effectiveness of security measures. Therefore, there is a need to develop systems that can detect anomalies more accurately and enable swift and appropriate countermeasures.

[0060] 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.

[0061] In this invention, the server includes a processing unit that receives information from a data collection device, an algorithm device that performs advanced analysis to recognize abnormal conditions, and a planning device that formulates countermeasures based on the abnormal conditions. This enables the integration of data from various sensors and the rapid and accurate detection of abnormal conditions.

[0062] A "data collection device" is a device that collects diverse information from the environment using sensors and other means.

[0063] A "processing device" is a device that receives information obtained from a collection device and processes it appropriately.

[0064] An "algorithmic device" is a device that highly analyzes received information and accurately recognizes abnormal conditions.

[0065] A "planning device" is a device that formulates the optimal countermeasures based on recognized abnormal conditions.

[0066] A "control device" is a device used to actually implement the countermeasures that have been formulated.

[0067] A "communication device" is a device used to report the details and status of operations performed by the control device to the user and to transmit necessary information.

[0068] To implement this invention, various sensors installed in homes or facilities are required. These sensors include a variety of devices such as WiFi-enabled communication sensors, temperature sensors for measuring temperature, humidity sensors for measuring humidity, sound sensors for capturing sound, and cameras for capturing visual information. These sensors play a role in monitoring various environmental data in real time and providing information to data collection devices.

[0069] The server receives data transmitted from the collection device and manages it appropriately using a processing unit. This processing utilizes an advanced algorithmic device employing a generative AI model. The algorithmic device is a key component for quickly recognizing anomalies from normal environmental conditions. This analysis makes it possible to detect unexpected events such as suspicious movements or sudden temperature changes.

[0070] When an anomaly is detected, the server, via a planning device, formulates the optimal response based on a pre-configured list of countermeasures. For example, in the area where the anomaly is detected, the control device may automatically turn on the lights or activate a security alarm.

[0071] Furthermore, users can receive real-time notifications via communication devices about abnormal conditions and the measures taken. If a dedicated application is installed on the user's terminal, they can immediately view the notifications and check the status of their home or facility. This allows users to respond flexibly to the situation.

[0072] For example, if a user enters a prompt message into the system such as "Instruct the system to strengthen security measures while I'm out," the server can automatically take immediate action, such as raising the security level and strengthening anomaly monitoring.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server receives data from various sensors within the home. Specifically, it collects temperature information from temperature sensors, humidity data from humidity sensors, sound levels from sound sensors, and video data from cameras. This input data is integrated by the server's processing unit and converted into a format that can be accessed in real time.

[0076] Step 2:

[0077] The received data is analyzed by an algorithmic device on the server. Using a generative AI model, it performs analysis to distinguish between normal and abnormal patterns. For example, it can detect a sudden temperature increase outside the normal temperature range or suspicious noises at night. If an abnormal condition is recognized based on the input data, it outputs a flag indicating the abnormality.

[0078] Step 3:

[0079] If an anomaly flag is raised, the server's planning device formulates security measures. Specifically, it determines countermeasures such as turning on lights or activating alarms based on the information obtained from sensors and the type of anomaly, and outputs the details of these measures.

[0080] Step 4:

[0081] Based on the formulated countermeasures, the terminals execute them through the control unit. For example, if an anomaly is detected, the lights in the designated room will automatically turn on, or a security alarm will sound. This implements a physical security measure.

[0082] Step 5:

[0083] The results of security measures are notified to users in real time via communication devices. Specifically, the actions taken and the current situation are reported via push notifications to the user's terminal or email. Upon receiving the notification, the user can send further instructions to the system as input and adjust the system's operation as needed.

[0084] (Application Example 1)

[0085] 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."

[0086] In recent years, despite the increasing importance of home security, conventional security systems have limitations in real-time situation monitoring and immediate response. It is difficult for users to quickly take necessary measures after detecting an anomaly, and insufficient responses contribute to security vulnerabilities. Furthermore, conventional systems require the effective integration of diverse sensor information and accurate recognition of anomaly patterns. Against this backdrop, there is a need for a system that ensures a high level of home security while reducing the burden on users.

[0087] 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.

[0088] In this invention, the server includes an information processing device means for receiving information from a collection means, an analysis device means for analyzing the received information and detecting anomalies, and a planning device means for determining safety measures based on the anomalies. This makes it possible to integrate multiple types of sensor information to quickly detect anomalies and immediately take optimal safety measures.

[0089] "Information gathering methods" refer to means of obtaining necessary information from various sensors placed within the home.

[0090] An "information processing device" is a device that receives information obtained from a collection device and performs the necessary processing.

[0091] "Analysis device means" refers to a device that analyzes information received from information processing device means and detects abnormalities from the normal state.

[0092] The "planning device means" is a device for determining the optimal safety measures based on the abnormalities detected by the analysis device means.

[0093] A "control device" is a device for physically implementing the safety measures determined by the planning device.

[0094] An "information provision device" is a device for notifying the user of the execution results and the status of anomaly detection of the control device.

[0095] An "environmental control device" is a device used to control lighting and alarms in a specific area when an abnormality is detected.

[0096] A "control device means" is a device that allows a user to remotely control the system using an information terminal.

[0097] A "user terminal" is a terminal used by a user to receive notifications from information provision devices and to monitor and operate the system.

[0098] A "generative AI model" is an artificial intelligence model used in the process of information analysis and anomaly detection, and includes algorithms for making predictions and judgments.

[0099] A "prompt message" is an instruction or question presented by an information-providing device to the user, intended to prompt the user for a response or instruction.

[0100] To implement this invention, it is first necessary to install various sensors in the home. This allows temperature sensors, humidity sensors, sound sensors, cameras, and WiFi sensors to collect environmental data in real time. The information acquired by these sensors is transmitted to a server, which is an information processing device.

[0101] The server executes an analysis device using a generated AI model and analyzes the acquired data in real time. In doing so, it integrates information from multiple sensors to detect patterns that are different from the normal and recognize anomalies. In this process, for example, suspicious movements or sudden temperature changes during the night, when it is normally quiet, may be detected.

[0102] When an anomaly is detected, the server's planning device quickly determines safety measures. Specifically, based on the type and location of the anomaly, it may automatically turn on the lights or activate the security alarm. Furthermore, the environmental control device can lock the digital lock and send an emergency alert as needed.

[0103] Furthermore, the user's smartphone terminal receives immediate notification through an information provision device. The notification includes the type of anomaly, its location, and the countermeasures taken. At this time, prompt messages are used to provide the user with specific instructions, such as "An abnormal movement has been detected. Please check." or "A temperature rise has been detected. Shall we turn off the heater?"

[0104] Users can directly monitor and control the system from their smartphones via a control device. This two-way operation allows users to respond flexibly to unforeseen circumstances. Thus, the present invention enhances home security and enables a quick and appropriate response to abnormal situations.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server receives data in real time from various sensors installed in the home. Inputs include temperature, humidity, audio, video, and Wi-Fi connection information. This data is temporarily stored by an information processing unit.

[0108] Step 2:

[0109] The server analyzes the collected data using analytical devices. It integrates diverse sensor information received as input and detects anomalies using a generated AI model. This process primarily employs statistical analysis and anomaly detection algorithms. This yields output regarding the type and location of the anomaly.

[0110] Step 3:

[0111] The server determines the optimal security measures based on the analysis results using a planning device. It receives the type and location information of the anomaly output by the AI ​​as input and determines actions such as activating the security alarm, turning on the lights, and locking the digital lock. This results in the output of a specific action plan as a security measure.

[0112] Step 4:

[0113] The terminal physically executes the planned security measures. It receives instructions from the server, operates smart lights, activates alarm systems, and controls digital locks. This ensures that the planned actions are actually carried out.

[0114] Step 5:

[0115] The server notifies the user's terminal of the results of the action and details of the anomaly. The information provision device formats the information regarding the anomaly detection and countermeasures and generates a prompt message. Specifically, a message such as "Anomaly activity has been detected. Please check." is sent. This notification is sent to the user, allowing them to understand the current situation and take additional action.

[0116] Step 6:

[0117] Users can perform further necessary operations via their user terminal. They receive notifications as input and output additional instructions according to prompts. For example, by selecting "off" in response to the prompt "Do you want to turn off the heater?", the user controls the device through the server. This two-way operation allows users to efficiently ensure home security.

[0118] 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.

[0119] To implement this invention, multiple sensors are installed in the home to collect data using WiFi, temperature, humidity, audio, cameras, etc. This data is transmitted in real time to a server by a terminal. The server analyzes the received data and uses an AI algorithm to detect abnormal movements and patterns. Furthermore, this invention incorporates an emotion engine to recognize the user's emotions, determining their emotional state based on audio and video data from the user.

[0120] The emotion information determined by the emotion engine is used as a factor in influencing the server's security action decisions and optimizing responses after anomaly detection. For example, if a user is in a state of tension, the system is adjusted to prompt a faster response than usual. Furthermore, user emotion information is notified in a customized format via notification methods. If it is detected that a user is experiencing stress, a notification can be sent to the user including calming recommendations.

[0121] When an anomaly is detected, the terminal takes appropriate security actions based on commands from the server. For example, if it is detected that a resident is not in a calm state of mind during an anomaly, the terminal's automatic locking and emergency contact functions will be enhanced. The results of these actions are immediately notified to the user, and it is possible to send further instructions to the system based on the results.

[0122] As a specific example, consider a scenario where an unusual sound is detected late at night, and based on video data obtained from a camera inside the house, it is detected that the user is in an unstable emotional state. In this case, in addition to normal security measures, the server immediately notifies selected relatives and security companies and takes all possible safety measures. In this way, the present invention provides more advanced security and safety management through emotional state analysis.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The device collects data from various sensors installed in the home. This includes data on temperature, humidity, sound, and camera footage, and is measured in real time.

[0126] Step 2:

[0127] The device sends the collected data to the server. This data includes user voice and video information for the emotion engine.

[0128] Step 3:

[0129] The server analyzes the received data. Using AI algorithms, it detects patterns that deviate from normal conditions and identifies anomalies.

[0130] Step 4:

[0131] The server analyzes audio and video data using an emotion engine to recognize the user's emotional state. This analysis includes factors such as voice tone and facial expressions.

[0132] Step 5:

[0133] The server plans appropriate security actions based on the type of anomaly and the user's emotional state. It develops flexible responses that take into account the level of risk and the user's emotional state.

[0134] Step 6:

[0135] The terminal will follow instructions from the server and execute planned security actions. If necessary, it will turn on lights, activate security alarms, and lock doors.

[0136] Step 7:

[0137] Users receive notifications based on the analysis results. These notifications include details of the anomaly, the security actions taken, and advice tailored to the user's emotions.

[0138] Step 8:

[0139] Based on the information provided, users can communicate additional instructions to the system via the application. This allows the system to issue instructions tailored to the user's situation if further action is required.

[0140] (Example 2)

[0141] 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 will be referred to as the "terminal."

[0142] In modern home environments, crime prevention and safety management are becoming increasingly important. However, conventional security systems often only perform simple anomaly detection and lack optimization through in-depth analysis of user emotions and behavioral patterns. Furthermore, the uniform notification system to users after anomaly detection may prevent adequate measures from being taken that are appropriate for each household and situation.

[0143] 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.

[0144] In this invention, the server includes means for receiving environmental data and user data from collection means, means for analyzing the received data using an AI algorithm to detect anomalies, and means for optimizing security actions by utilizing the emotion score obtained by the analysis means. This enables not only simple anomaly detection but also flexible and customized security and safety management that responds to the user's emotional state.

[0145] "Collection means" refers to devices or functions that acquire environmental data and user data and transmit them to a server.

[0146] A "server means" is a central control unit or system for storing and analyzing received data.

[0147] "Analysis means" refers to a function that uses AI algorithms to analyze received data and detect anomalies.

[0148] The "planning method" is a function that optimizes crime prevention actions by utilizing emotion scores based on information obtained through analysis methods.

[0149] "Terminal means" refers to devices or systems used to physically carry out planned crime prevention actions.

[0150] A "notification method" is a communication function that informs the user of the results of the terminal device's execution and recommendations.

[0151] An "AI algorithm" is a computational method based on artificial intelligence technology used for data analysis and prediction.

[0152] An "emotion score" is an index used to numerically evaluate a user's emotional state based on audio and video data.

[0153] This invention provides an embodiment of a system that enhances security and user emotional management within the home. Its specific configuration and operation are described below.

[0154] The system collects environmental and user data using multiple sensors. These sensors include temperature sensors, humidity sensors, sound sensors, and cameras. These sensors collect data and transmit it to the device in real time via Wi-Fi.

[0155] The terminal functions as a gateway to receive data from this sensor and send it to the server. This communication method uses the HTTPS protocol to ensure data security.

[0156] The server is the central device that analyzes the received data and uses AI algorithms to detect anomalies. The AI ​​algorithms learn models based on daily life patterns and identify anomalous events. Deep learning techniques can be used in particular for this analysis. Furthermore, the server analyzes audio and video data, measures the user's emotional state through an emotion engine, and generates an emotion score.

[0157] The emotional score influences the planning of security actions. When an anomaly is detected, the server plans the optimal security action while taking into account the acquired emotional information. This process ensures that security actions are not merely a reaction to an anomaly, but are more appropriately adjusted by considering the user's mental state.

[0158] Users receive the results from their device via a notification system. These notifications include a description of the current situation and suggested actions, customized based on the user's emotional state. For example, if the system determines the user is stressed, a notification will be sent containing advice to help them calm down.

[0159] For example, if an unusual sound is detected late at night, the server analyzes the video data from the camera and determines that the user is in an unstable state. In this case, relatives and security companies are immediately notified, and appropriate security measures are taken.

[0160] An example of a prompt to the generating AI model is, "Please suggest the best security action to take when a user is emotionally unstable and an unusual noise is detected late at night." Based on this prompt, the AI ​​generates specific countermeasures to support the overall operation of the system.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The device collects environmental and user data from sensors installed in the home. Inputs are real-time data obtained from temperature, humidity, sound, and camera sensors. The device then structures this data and prepares it for transmission to a server via Wi-Fi. Outputs are structured JSON data.

[0164] Step 2:

[0165] The terminal sends the collected data to the server. The input is the structured data generated in step 1. The data is securely sent to the server using the HTTPS protocol. The output is the environment data and user data received by the server.

[0166] Step 3:

[0167] The server analyzes the received data. The input is structured data sent from the terminal. It applies an AI algorithm and evaluates the data using deep learning techniques. It compares the data to normal conditions and detects abnormal patterns. The output is result data indicating whether or not an anomaly was detected.

[0168] Step 4:

[0169] The server analyzes the user's emotional state using an emotion engine. Input consists of audio and video data. It generates a user emotion score by combining speech recognition and image analysis technologies. The output is numerical emotion score data.

[0170] Step 5:

[0171] The server plans security actions based on the analysis results. The inputs are anomaly detection results and sentiment scores. An AI model is used to optimize the necessary actions. If the sentiment score is high, rapid response measures are considered. The output is a specific security action plan.

[0172] Step 6:

[0173] The terminal executes security actions based on instructions from the server. The input is the security action plan, which includes actions such as automatically locking doors, activating alarms, and sending emergency notifications to selected contacts. The output is the result of the actions performed.

[0174] Step 7:

[0175] The user receives notifications about the execution results and recommended actions. Input is the processing result from the terminal. The notification means provides the user with an explanation of the anomaly and corresponding suggestions and advice. Output is customized notification information sent to the user.

[0176] (Application Example 2)

[0177] 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 device 14 will be referred to as the "terminal."

[0178] In modern society, crime prevention and safety are extremely important issues, but conventional systems do not adequately address the emotional state of users in their living spaces to provide efficient alarm responses. As a result, unnecessary alarms may be triggered, or a quick response may not be possible when truly necessary. Therefore, there is a need for a system that considers the emotional state of users when detecting abnormalities or dangers, and that simultaneously optimizes crime prevention and a sense of security.

[0179] 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.

[0180] In this invention, the server includes an information processing device, an analysis device, and a planning device. This enables the formulation of an action plan that reflects abnormal and emotional states.

[0181] "Data collection means" refers to multiple sensors installed in a home that collect information such as Wi-Fi, temperature, humidity, sound, and camera data.

[0182] "Information processing device means" refers to a device that receives collected data and transmits it to a server.

[0183] "Analysis means" refers to algorithms and programs used to detect anomalies or the emotional state of users using the received data.

[0184] "Planning means" refers to the process of determining the optimal action plan based on the results detected by the analysis means.

[0185] "Terminal device means" refers to a device that performs operations on physical equipment or systems in accordance with a predetermined action plan.

[0186] "Notification device means" refers to a device that provides users with information and recommendations tailored to their actions and emotional state.

[0187] An "anomaly" refers to movements, sounds, or other phenomena that deviate from the normal patterns based on sensor data.

[0188] "Emotional state" refers to the user's psychological state as inferred from voice and camera data.

[0189] The system for implementing this invention consists of multiple sensors installed in the home and a device that collects data from them. This system collects information such as WiFi, temperature, humidity, voice, and camera data, and transmits it to a server in real time via an information processing device. The server analyzes the received data using an AI algorithm to detect anomalies and the user's emotional state. AI frameworks such as TENSORFLOW® and PyTorch are often used for the analysis.

[0190] Based on the analyzed data, the planning system develops an action plan that takes into account abnormalities and emotional states. In particular, if the user is determined to be experiencing tension or an unstable emotional state, the plan is modified to ensure faster and more appropriate action than usual. Once the action plan is determined, the terminal device performs the actual operations, implementing security measures and notifying the user. Notifications are sent to the user's smartphone or other devices using the Twilio API, etc.

[0191] Furthermore, the notification device uses generative AI models such as OpenAI's GPT to generate customized messages tailored to the user's emotional state, providing suggestions and information to alleviate stress.

[0192] For example, if an unusual sound is detected late at night and the camera footage indicates that the user is in an unstable state, the server will immediately and automatically notify selected relatives or security organizations. Additionally, a suggested music playlist for relaxation will be sent to the user's smartphone. An example of a prompt for the generating AI model is, "Generate a message to provide reassurance based on the user's emotional state."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server receives data from multiple sensors installed within the home. Inputs include temperature, humidity, audio, and camera footage. This data is integrated in real time and temporarily stored in a database.

[0196] Step 2:

[0197] The server analyzes the received data using AI frameworks such as TensorFlow and PyTorch. The input is the sensor data obtained in step 1, and the server performs data processing to detect anomalies and recognize the type and pattern of the anomalies. The output is information about the presence and type of anomalies.

[0198] Step 3:

[0199] The server uses a generative AI model (such as OpenAI's GPT) based on the analysis results to recognize the user's emotional state. The input consists of the anomaly information from step 2 and sensor data, and the emotion recognition algorithm performs data calculations to estimate the emotional state. The output is information about the user's emotional state.

[0200] Step 4:

[0201] The server formulates an action plan based on the analysis results and the user's emotional state. The input is the output data from steps 2 and 3, and the server processes this data to determine the optimal crime prevention action for the given situation. The output is information related to the action plan.

[0202] Step 5:

[0203] The terminal receives an action plan provided by the server and performs physical or digital actions based on it. The input is the action plan from step 4, and the necessary measures are implemented. The output is the status of the crime prevention actions.

[0204] Step 6:

[0205] The server uses the Twilio API to notify the user of the execution results from the notification device. The input is the status of the crime prevention action performed in step 5, and the data is processed to generate a message according to the emotional state. The output is the content of the notification sent to the user's smartphone.

[0206] 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.

[0207] Data generation model 58 is a 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] To implement this invention, multiple sensors installed in the home must collect data in real time. This involves a variety of sensors, including WiFi sensors, temperature sensors, humidity sensors, sound sensors, and cameras. These sensors continuously monitor the conditions within the home and transmit the data to a server at specified time intervals.

[0223] The server is responsible for analyzing the received data. AI algorithms are used for data analysis, recognizing anomalies by detecting patterns that deviate from normal conditions. For example, it recognizes suspicious movement during periods of low activity, sudden temperature changes, or unusual sounds as anomalies.

[0224] When an anomaly is detected, the server plans the optimal countermeasures based on a pre-configured security plan. This includes specific actions to enhance security. The planned actions are executed by various devices located within the home.

[0225] The device performs appropriate security actions based on instructions from the server. This includes automatically turning on lights in areas where an anomaly is detected and activating security alarms. It also ensures family safety by automatically locking digital locks or sending emergency calls depending on the level of risk detected by the anomaly.

[0226] Finally, users can receive detailed information about anomalies in real time through the application. Notifications include the type of anomaly, its location, and the actions taken, allowing users to monitor the situation at home via their smartphone or computer. After receiving a notification, users can also send further instructions to the system as needed, enabling them to respond flexibly to unexpected situations.

[0227] In this way, the present invention achieves highly accurate home security and monitoring through multiple sensors, AI analysis, and automated terminal control.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The device collects data from multiple sensors installed in the home. These include sensors for temperature, humidity, sound, and cameras, and the data is measured at regular intervals.

[0231] Step 2:

[0232] The device sends the collected data to the server. This data includes numerical values ​​and image data based on the timestamp and sensor type.

[0233] Step 3:

[0234] The server analyzes the received data and compares it to normal patterns using an AI algorithm. This identifies data points that are considered anomaly.

[0235] Step 4:

[0236] When an anomaly is detected, the server determines the optimal security action based on a pre-configured security plan. The planned measures include risk assessment.

[0237] Step 5:

[0238] The terminal receives instructions from the server and executes planned security actions. These actions include specific measures such as turning on lights, activating security alarms, and locking digital locks.

[0239] Step 6:

[0240] Users receive notifications of anomaly detection through the application. The notifications include details of the location, nature of the anomaly, and the actions taken.

[0241] Step 7:

[0242] Users can use the application to send additional instructions to the system, enabling flexible responses to changing situations.

[0243] (Example 1)

[0244] 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."

[0245] In today's world, ensuring the safety of homes and facilities with high accuracy requires improving the efficiency and reliability of anomaly detection systems. Conventional systems can miss anomalies or, conversely, misidentify them, which reduces the effectiveness of security measures. Therefore, there is a need to develop systems that can detect anomalies more accurately and enable swift and appropriate countermeasures.

[0246] 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.

[0247] In this invention, the server includes a processing unit that receives information from a data collection device, an algorithm device that performs advanced analysis to recognize abnormal conditions, and a planning device that formulates countermeasures based on the abnormal conditions. This enables the integration of data from various sensors and the rapid and accurate detection of abnormal conditions.

[0248] A "data collection device" is a device that collects diverse information from the environment using sensors and other means.

[0249] A "processing device" is a device that receives information obtained from a collection device and processes it appropriately.

[0250] An "algorithmic device" is a device that highly analyzes received information and accurately recognizes abnormal conditions.

[0251] A "planning device" is a device that formulates the optimal countermeasures based on recognized abnormal conditions.

[0252] A "control device" is a device used to actually implement the countermeasures that have been formulated.

[0253] A "communication device" is a device used to report the details and status of operations performed by the control device to the user and to transmit necessary information.

[0254] To implement this invention, various sensors installed in homes or facilities are required. These sensors include a variety of devices such as WiFi-enabled communication sensors, temperature sensors for measuring temperature, humidity sensors for measuring humidity, sound sensors for capturing sound, and cameras for capturing visual information. These sensors play a role in monitoring various environmental data in real time and providing information to data collection devices.

[0255] The server receives data transmitted from the collection device and manages it appropriately using a processing unit. This processing utilizes an advanced algorithmic device employing a generative AI model. The algorithmic device is a key component for quickly recognizing anomalies from normal environmental conditions. This analysis makes it possible to detect unexpected events such as suspicious movements or sudden temperature changes.

[0256] When an anomaly is detected, the server, via a planning device, formulates the optimal response based on a pre-configured list of countermeasures. For example, in the area where the anomaly is detected, the control device may automatically turn on the lights or activate a security alarm.

[0257] Furthermore, users can receive real-time notifications via communication devices about abnormal conditions and the measures taken. If a dedicated application is installed on the user's terminal, they can immediately view the notifications and check the status of their home or facility. This allows users to respond flexibly to the situation.

[0258] For example, if a user enters a prompt message into the system such as "Instruct the system to strengthen security measures while I'm out," the server can automatically take immediate action, such as raising the security level and strengthening anomaly monitoring.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server receives data from various sensors within the home. Specifically, it collects temperature information from temperature sensors, humidity data from humidity sensors, sound levels from sound sensors, and video data from cameras. This input data is integrated by the server's processing unit and converted into a format that can be accessed in real time.

[0262] Step 2:

[0263] The received data is analyzed by an algorithmic device on the server. Using a generative AI model, it performs analysis to distinguish between normal and abnormal patterns. For example, it can detect a sudden temperature increase outside the normal temperature range or suspicious noises at night. If an abnormal condition is recognized based on the input data, it outputs a flag indicating the abnormality.

[0264] Step 3:

[0265] If an anomaly flag is raised, the server's planning device formulates security measures. Specifically, it determines countermeasures such as turning on lights or activating alarms based on the information obtained from sensors and the type of anomaly, and outputs the details of these measures.

[0266] Step 4:

[0267] Based on the formulated countermeasures, the terminals execute them through the control unit. For example, if an anomaly is detected, the lights in the designated room will automatically turn on, or a security alarm will sound. This implements a physical security measure.

[0268] Step 5:

[0269] The results of security measures are notified to users in real time via communication devices. Specifically, the actions taken and the current situation are reported via push notifications to the user's terminal or email. Upon receiving the notification, the user can send further instructions to the system as input and adjust the system's operation as needed.

[0270] (Application Example 1)

[0271] 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."

[0272] In recent years, despite the increasing importance of home security, conventional security systems have limitations in real-time situation monitoring and immediate response. It is difficult for users to quickly take necessary measures after detecting an anomaly, and insufficient responses contribute to security vulnerabilities. Furthermore, conventional systems require the effective integration of diverse sensor information and accurate recognition of anomaly patterns. Against this backdrop, there is a need for a system that ensures a high level of home security while reducing the burden on users.

[0273] 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.

[0274] In this invention, the server includes an information processing device means for receiving information from a collection means, an analysis device means for analyzing the received information and detecting anomalies, and a planning device means for determining safety measures based on the anomalies. This makes it possible to integrate multiple types of sensor information to quickly detect anomalies and immediately take optimal safety measures.

[0275] "Information gathering methods" refer to means of obtaining necessary information from various sensors placed within the home.

[0276] An "information processing device" is a device that receives information obtained from a collection device and performs the necessary processing.

[0277] "Analysis device means" refers to a device that analyzes information received from information processing device means and detects abnormalities from the normal state.

[0278] The "planning device means" is a device for determining the optimal safety measures based on the abnormalities detected by the analysis device means.

[0279] A "control device" is a device for physically implementing the safety measures determined by the planning device.

[0280] An "information provision device" is a device for notifying the user of the execution results and the status of anomaly detection of the control device.

[0281] An "environmental control device" is a device used to control lighting and alarms in a specific area when an abnormality is detected.

[0282] A "control device means" is a device that allows a user to remotely control the system using an information terminal.

[0283] A "user terminal" is a terminal used by a user to receive notifications from information provision devices and to monitor and operate the system.

[0284] The "generative AI model" is an artificial intelligence model used in the process of information analysis and anomaly detection, and includes algorithms for making predictions and judgments.

[0285] The "prompt sentence" is an instruction sentence or question sentence presented by the information providing device means to the user, and is used to prompt the user's response or instruction.

[0286] To implement this invention, first, various sensors need to be installed in the home. As a result, a temperature sensor, a humidity sensor, a voice sensor, a camera, and a WiFi sensor as collection means can collect their respective environmental data in real time. The information acquired by these sensors is transmitted to a server, which is the information processing device means.

[0287] The server executes an analysis device means using a generative AI model and analyzes the acquired data in real time. At that time, it integrates multiple sensor information to detect patterns different from normal and recognizes anomalies. In this process, for example, suspicious movements or rapid temperature changes in the dead of night when it is usually quiet are detected.

[0288] When an anomaly is detected, the planning device means of the server quickly determines safety measures. Specifically, based on the type and location of the anomaly, it automatically turns on the lighting or activates the security alarm. It is also possible to lock the digital key as needed by the environmental control device means and make an emergency call.

[0289] Furthermore, an immediate notification is sent to the smartphone, which is the user terminal, through the information providing device means. The notification content includes the type of anomaly, the location of occurrence, and the measures taken. At this time, using the prompt sentence, specific instructions such as "Suspicious movement has been detected. Please confirm." or "A temperature increase has been detected. Do you want to turn off the heater?" are provided to the user.

[0290] Users can directly monitor and control the system from their smartphones via a control device. This two-way operation allows users to respond flexibly to unforeseen circumstances. Thus, the present invention enhances home security and enables a quick and appropriate response to abnormal situations.

[0291] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0292] Step 1:

[0293] The server receives data in real time from various sensors installed in the home. Inputs include temperature, humidity, audio, video, and Wi-Fi connection information. This data is temporarily stored by an information processing unit.

[0294] Step 2:

[0295] The server analyzes the collected data using analytical devices. It integrates diverse sensor information received as input and detects anomalies using a generated AI model. This process primarily employs statistical analysis and anomaly detection algorithms. This yields output regarding the type and location of the anomaly.

[0296] Step 3:

[0297] The server determines the optimal security measures based on the analysis results using a planning device. It receives the type and location information of the anomaly output by the AI ​​as input and determines actions such as activating the security alarm, turning on the lights, and locking the digital lock. This results in the output of a specific action plan as a security measure.

[0298] Step 4:

[0299] The terminal physically executes the planned security measures. It receives instructions from the server, operates smart lights, activates alarm systems, and controls digital locks. This ensures that the planned actions are actually carried out.

[0300] Step 5:

[0301] The server notifies the user's terminal of the results of the action and details of the anomaly. The information provision device formats the information regarding the anomaly detection and countermeasures and generates a prompt message. Specifically, a message such as "Anomaly activity has been detected. Please check." is sent. This notification is sent to the user, allowing them to understand the current situation and take additional action.

[0302] Step 6:

[0303] Users can perform further necessary operations via their user terminal. They receive notifications as input and output additional instructions according to prompts. For example, by selecting "off" in response to the prompt "Do you want to turn off the heater?", the user controls the device through the server. This two-way operation allows users to efficiently ensure home security.

[0304] 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.

[0305] To implement this invention, multiple sensors are installed in the home to collect data using WiFi, temperature, humidity, audio, cameras, etc. This data is transmitted in real time to a server by a terminal. The server analyzes the received data and uses an AI algorithm to detect abnormal movements and patterns. Furthermore, this invention incorporates an emotion engine to recognize the user's emotions, determining their emotional state based on audio and video data from the user.

[0306] The emotional information judged by the emotion engine is regarded as a factor that affects the determination of the server's security actions, and optimizes the response after abnormal detection. For example, when the user is in a tense state, the system is adjusted to prompt a faster response than usual. In addition, the user's emotional information is notified with content customized by the notification means. When it is detected that the user is feeling stressed, it is possible to send a notification to the user that includes recommendations to calm down.

[0307] When an abnormality is detected, the terminal executes appropriate security actions based on an instruction from the server. For example, when it is recognized that the resident's emotion is not calm during an abnormality, the automatic locking and emergency contact functions from the terminal are strengthened. The execution result is immediately notified to the user, and it is possible to send further instructions to the system based on the result.

[0308] As a specific example, consider the case where an abnormal sound is detected late at night, and it is detected based on the video data obtained from the camera in the house at that time that the user is in an unstable emotional state. In that case, in addition to the normal security measures, the server executes an immediate notification to the selected relatives or security company and takes safety measures as much as possible. In this way, the present invention provides more advanced security and safety management through analysis of the emotional state.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The terminal collects data from various sensors installed in the home. This includes data such as temperature, humidity, voice, and camera, which are measured in real time.

[0312] Step 2:

[0313] The terminal transmits the collected data to the server. The data also includes the user's voice and video information for the emotion engine.

[0314] Step 3:

[0315] The server analyzes the received data. Using AI algorithms, it detects patterns that deviate from normal conditions and identifies anomalies.

[0316] Step 4:

[0317] The server analyzes audio and video data using an emotion engine to recognize the user's emotional state. This analysis includes factors such as voice tone and facial expressions.

[0318] Step 5:

[0319] The server plans appropriate security actions based on the type of anomaly and the user's emotional state. It develops flexible responses that take into account the level of risk and the user's emotional state.

[0320] Step 6:

[0321] The terminal will follow instructions from the server and execute planned security actions. If necessary, it will turn on lights, activate security alarms, and lock doors.

[0322] Step 7:

[0323] Users receive notifications based on the analysis results. These notifications include details of the anomaly, the security actions taken, and advice tailored to the user's emotions.

[0324] Step 8:

[0325] Based on the information provided, users can communicate additional instructions to the system via the application. This allows the system to issue instructions tailored to the user's situation if further action is required.

[0326] (Example 2)

[0327] 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".

[0328] In modern home environments, crime prevention and safety management are becoming increasingly important. However, conventional security systems often only perform simple anomaly detection and lack optimization through in-depth analysis of user emotions and behavioral patterns. Furthermore, the uniform notification system to users after anomaly detection may prevent adequate measures from being taken that are appropriate for each household and situation.

[0329] 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.

[0330] In this invention, the server includes means for receiving environmental data and user data from collection means, means for analyzing the received data using an AI algorithm to detect anomalies, and means for optimizing security actions by utilizing the emotion score obtained by the analysis means. This enables not only simple anomaly detection but also flexible and customized security and safety management that responds to the user's emotional state.

[0331] "Collection means" refers to devices or functions that acquire environmental data and user data and transmit them to a server.

[0332] A "server means" is a central control unit or system for storing and analyzing received data.

[0333] "Analysis means" refers to a function that uses AI algorithms to analyze received data and detect anomalies.

[0334] The "planning method" is a function that optimizes crime prevention actions by utilizing emotion scores based on information obtained through analysis methods.

[0335] "Terminal means" refers to devices or systems used to physically carry out planned crime prevention actions.

[0336] A "notification method" is a communication function that informs the user of the results of the terminal device's execution and recommendations.

[0337] An "AI algorithm" is a computational method based on artificial intelligence technology used for data analysis and prediction.

[0338] An "emotion score" is an index used to numerically evaluate a user's emotional state based on audio and video data.

[0339] This invention provides an embodiment of a system that enhances security and user emotional management within the home. Its specific configuration and operation are described below.

[0340] The system collects environmental and user data using multiple sensors. These sensors include temperature sensors, humidity sensors, sound sensors, and cameras. These sensors collect data and transmit it to the device in real time via Wi-Fi.

[0341] The terminal functions as a gateway to receive data from this sensor and send it to the server. This communication method uses the HTTPS protocol to ensure data security.

[0342] The server is the central device that analyzes the received data and uses AI algorithms to detect anomalies. The AI ​​algorithms learn models based on daily life patterns and identify anomalous events. Deep learning techniques can be used in particular for this analysis. Furthermore, the server analyzes audio and video data, measures the user's emotional state through an emotion engine, and generates an emotion score.

[0343] The emotional score influences the planning of security actions. When an anomaly is detected, the server plans the optimal security action while taking into account the acquired emotional information. This process ensures that security actions are not merely a reaction to an anomaly, but are more appropriately adjusted by considering the user's mental state.

[0344] Users receive the results from their device via a notification system. These notifications include a description of the current situation and suggested actions, customized based on the user's emotional state. For example, if the system determines the user is stressed, a notification will be sent containing advice to help them calm down.

[0345] For example, if an unusual sound is detected late at night, the server analyzes the video data from the camera and determines that the user is in an unstable state. In this case, relatives and security companies are immediately notified, and appropriate security measures are taken.

[0346] An example of a prompt to the generating AI model is, "Please suggest the best security action to take when a user is emotionally unstable and an unusual noise is detected late at night." Based on this prompt, the AI ​​generates specific countermeasures to support the overall operation of the system.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The device collects environmental and user data from sensors installed in the home. Inputs are real-time data obtained from temperature, humidity, sound, and camera sensors. The device then structures this data and prepares it for transmission to a server via Wi-Fi. Outputs are structured JSON data.

[0350] Step 2:

[0351] The terminal sends the collected data to the server. The input is the structured data generated in step 1. The data is securely sent to the server using the HTTPS protocol. The output is the environment data and user data received by the server.

[0352] Step 3:

[0353] The server analyzes the received data. The input is structured data sent from the terminal. It applies an AI algorithm and evaluates the data using deep learning techniques. It compares the data to normal conditions and detects abnormal patterns. The output is result data indicating whether or not an anomaly was detected.

[0354] Step 4:

[0355] The server analyzes the user's emotional state using an emotion engine. Input consists of audio and video data. It generates a user emotion score by combining speech recognition and image analysis technologies. The output is numerical emotion score data.

[0356] Step 5:

[0357] The server plans security actions based on the analysis results. The inputs are anomaly detection results and sentiment scores. An AI model is used to optimize the necessary actions. If the sentiment score is high, rapid response measures are considered. The output is a specific security action plan.

[0358] Step 6:

[0359] The terminal executes security actions based on instructions from the server. The input is the security action plan, which includes actions such as automatically locking doors, activating alarms, and sending emergency notifications to selected contacts. The output is the result of the actions performed.

[0360] Step 7:

[0361] The user receives notifications about the execution results and recommended actions. Input is the processing result from the terminal. The notification means provides the user with an explanation of the anomaly and corresponding suggestions and advice. Output is customized notification information sent to the user.

[0362] (Application Example 2)

[0363] 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."

[0364] In modern society, crime prevention and safety are extremely important issues, but conventional systems do not adequately address the emotional state of users in their living spaces to provide efficient alarm responses. As a result, unnecessary alarms may be triggered, or a quick response may not be possible when truly necessary. Therefore, there is a need for a system that considers the emotional state of users when detecting abnormalities or dangers, and that simultaneously optimizes crime prevention and a sense of security.

[0365] 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.

[0366] In this invention, the server includes an information processing device, an analysis device, and a planning device. This enables the formulation of an action plan that reflects abnormal and emotional states.

[0367] "Data collection means" refers to multiple sensors installed in a home that collect information such as Wi-Fi, temperature, humidity, sound, and camera data.

[0368] "Information processing device means" refers to a device that receives collected data and transmits it to a server.

[0369] "Analysis means" refers to algorithms and programs used to detect anomalies or the emotional state of users using the received data.

[0370] "Planning means" refers to the process of determining the optimal action plan based on the results detected by the analysis means.

[0371] "Terminal device means" refers to a device that performs operations on physical equipment or systems in accordance with a predetermined action plan.

[0372] "Notification device means" refers to a device that provides users with information and recommendations tailored to their actions and emotional state.

[0373] An "anomaly" refers to movements, sounds, or other phenomena that deviate from the normal patterns based on sensor data.

[0374] "Emotional state" refers to the user's psychological state as inferred from voice and camera data.

[0375] The system for implementing this invention consists of multiple sensors installed in the home and a device that collects data from them. This system collects information such as WiFi, temperature, humidity, voice, and camera data, and transmits it to a server in real time via an information processing device. The server analyzes the received data using an AI algorithm to detect anomalies and the user's emotional state. AI frameworks such as TensorFlow and PyTorch are often used for the analysis.

[0376] Based on the analyzed data, the planning system develops an action plan that takes into account abnormalities and emotional states. In particular, if the user is determined to be experiencing tension or an unstable emotional state, the plan is modified to ensure faster and more appropriate action than usual. Once the action plan is determined, the terminal device performs the actual operations, implementing security measures and notifying the user. Notifications are sent to the user's smartphone or other devices using the Twilio API, etc.

[0377] Furthermore, the notification device uses generative AI models such as OpenAI's GPT to generate customized messages tailored to the user's emotional state, providing suggestions and information to alleviate stress.

[0378] For example, if an unusual sound is detected late at night and the camera footage indicates that the user is in an unstable state, the server will immediately and automatically notify selected relatives or security organizations. Additionally, a suggested music playlist for relaxation will be sent to the user's smartphone. An example of a prompt for the generating AI model is, "Generate a message to provide reassurance based on the user's emotional state."

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The server receives data from multiple sensors installed within the home. Inputs include temperature, humidity, audio, and camera footage. This data is integrated in real time and temporarily stored in a database.

[0382] Step 2:

[0383] The server analyzes the received data using AI frameworks such as TensorFlow and PyTorch. The input is the sensor data obtained in step 1, and the server performs data processing to detect anomalies and recognize the type and pattern of the anomalies. The output is information about the presence and type of anomalies.

[0384] Step 3:

[0385] The server uses a generative AI model (such as OpenAI's GPT) based on the analysis results to recognize the user's emotional state. The input consists of the anomaly information from step 2 and sensor data, and the emotion recognition algorithm performs data calculations to estimate the emotional state. The output is information about the user's emotional state.

[0386] Step 4:

[0387] The server formulates an action plan based on the analysis results and the user's emotional state. The input is the output data from steps 2 and 3, and the server processes this data to determine the optimal crime prevention action for the given situation. The output is information related to the action plan.

[0388] Step 5:

[0389] The terminal receives an action plan provided by the server and performs physical or digital actions based on it. The input is the action plan from step 4, and the necessary measures are implemented. The output is the status of the crime prevention actions.

[0390] Step 6:

[0391] The server uses the Twilio API to notify the user of the execution results from the notification device. The input is the status of the crime prevention action performed in step 5, and the data is processed to generate a message according to the emotional state. The output is the content of the notification sent to the user's smartphone.

[0392] 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.

[0393] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] To implement this invention, multiple sensors installed in the home must collect data in real time. This involves a variety of sensors, including WiFi sensors, temperature sensors, humidity sensors, sound sensors, and cameras. These sensors continuously monitor the conditions within the home and transmit the data to a server at specified time intervals.

[0409] The server is responsible for analyzing the received data. AI algorithms are used for data analysis, recognizing anomalies by detecting patterns that deviate from normal conditions. For example, it recognizes suspicious movement during periods of low activity, sudden temperature changes, or unusual sounds as anomalies.

[0410] When an anomaly is detected, the server plans the optimal countermeasures based on a pre-configured security plan. This includes specific actions to enhance security. The planned actions are executed by various devices located within the home.

[0411] The device performs appropriate security actions based on instructions from the server. This includes automatically turning on lights in areas where an anomaly is detected and activating security alarms. It also ensures family safety by automatically locking digital locks or sending emergency calls depending on the level of risk detected by the anomaly.

[0412] Finally, users can receive detailed information about anomalies in real time through the application. Notifications include the type of anomaly, its location, and the actions taken, allowing users to monitor the situation at home via their smartphone or computer. After receiving a notification, users can also send further instructions to the system as needed, enabling them to respond flexibly to unexpected situations.

[0413] In this way, the present invention achieves highly accurate home security and monitoring through multiple sensors, AI analysis, and automated terminal control.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The device collects data from multiple sensors installed in the home. These include sensors for temperature, humidity, sound, and cameras, and the data is measured at regular intervals.

[0417] Step 2:

[0418] The device sends the collected data to the server. This data includes numerical values ​​and image data based on the timestamp and sensor type.

[0419] Step 3:

[0420] The server analyzes the received data and compares it to normal patterns using an AI algorithm. This identifies data points that are considered anomaly.

[0421] Step 4:

[0422] When an anomaly is detected, the server determines the optimal security action based on a pre-configured security plan. The planned measures include risk assessment.

[0423] Step 5:

[0424] The terminal receives instructions from the server and executes planned security actions. These actions include specific measures such as turning on lights, activating security alarms, and locking digital locks.

[0425] Step 6:

[0426] Users receive notifications of anomaly detection through the application. The notifications include details of the location, nature of the anomaly, and the actions taken.

[0427] Step 7:

[0428] Users can use the application to send additional instructions to the system, enabling flexible responses to changing situations.

[0429] (Example 1)

[0430] 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."

[0431] In today's world, ensuring the safety of homes and facilities with high accuracy requires improving the efficiency and reliability of anomaly detection systems. Conventional systems can miss anomalies or, conversely, misidentify them, which reduces the effectiveness of security measures. Therefore, there is a need to develop systems that can detect anomalies more accurately and enable swift and appropriate countermeasures.

[0432] 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.

[0433] In this invention, the server includes a processing unit that receives information from a data collection device, an algorithm device that performs advanced analysis to recognize abnormal conditions, and a planning device that formulates countermeasures based on the abnormal conditions. This enables the integration of data from various sensors and the rapid and accurate detection of abnormal conditions.

[0434] A "data collection device" is a device that collects diverse information from the environment using sensors and other means.

[0435] A "processing device" is a device that receives information obtained from a collection device and processes it appropriately.

[0436] An "algorithmic device" is a device that highly analyzes received information and accurately recognizes abnormal conditions.

[0437] A "planning device" is a device that formulates the optimal countermeasures based on recognized abnormal conditions.

[0438] A "control device" is a device used to actually implement the countermeasures that have been formulated.

[0439] A "communication device" is a device used to report the details and status of operations performed by the control device to the user and to transmit necessary information.

[0440] To implement this invention, various sensors installed in homes or facilities are required. These sensors include a variety of devices such as WiFi-enabled communication sensors, temperature sensors for measuring temperature, humidity sensors for measuring humidity, sound sensors for capturing sound, and cameras for capturing visual information. These sensors play a role in monitoring various environmental data in real time and providing information to data collection devices.

[0441] The server receives data transmitted from the collection device and manages it appropriately using a processing unit. This processing utilizes an advanced algorithmic device employing a generative AI model. The algorithmic device is a key component for quickly recognizing anomalies from normal environmental conditions. This analysis makes it possible to detect unexpected events such as suspicious movements or sudden temperature changes.

[0442] When an anomaly is detected, the server, via a planning device, formulates the optimal response based on a pre-configured list of countermeasures. For example, in the area where the anomaly is detected, the control device may automatically turn on the lights or activate a security alarm.

[0443] Furthermore, users can receive real-time notifications via communication devices about abnormal conditions and the measures taken. If a dedicated application is installed on the user's terminal, they can immediately view the notifications and check the status of their home or facility. This allows users to respond flexibly to the situation.

[0444] For example, if a user enters a prompt message into the system such as "Instruct the system to strengthen security measures while I'm out," the server can automatically take immediate action, such as raising the security level and strengthening anomaly monitoring.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] The server receives data from various sensors within the home. Specifically, it collects temperature information from temperature sensors, humidity data from humidity sensors, sound levels from sound sensors, and video data from cameras. This input data is integrated by the server's processing unit and converted into a format that can be accessed in real time.

[0448] Step 2:

[0449] The received data is analyzed by an algorithmic device on the server. Using a generative AI model, it performs analysis to distinguish between normal and abnormal patterns. For example, it can detect a sudden temperature increase outside the normal temperature range or suspicious noises at night. If an abnormal condition is recognized based on the input data, it outputs a flag indicating the abnormality.

[0450] Step 3:

[0451] If an anomaly flag is raised, the server's planning device formulates security measures. Specifically, it determines countermeasures such as turning on lights or activating alarms based on the information obtained from sensors and the type of anomaly, and outputs the details of these measures.

[0452] Step 4:

[0453] Based on the formulated countermeasures, the terminals execute them through the control unit. For example, if an anomaly is detected, the lights in the designated room will automatically turn on, or a security alarm will sound. This implements a physical security measure.

[0454] Step 5:

[0455] The results of security measures are notified to users in real time via communication devices. Specifically, the actions taken and the current situation are reported via push notifications to the user's terminal or email. Upon receiving the notification, the user can send further instructions to the system as input and adjust the system's operation as needed.

[0456] (Application Example 1)

[0457] 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."

[0458] In recent years, despite the increasing importance of home security, conventional security systems have limitations in real-time situation monitoring and immediate response. It is difficult for users to quickly take necessary measures after detecting an anomaly, and insufficient responses contribute to security vulnerabilities. Furthermore, conventional systems require the effective integration of diverse sensor information and accurate recognition of anomaly patterns. Against this backdrop, there is a need for a system that ensures a high level of home security while reducing the burden on users.

[0459] 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.

[0460] In this invention, the server includes an information processing device means for receiving information from a collection means, an analysis device means for analyzing the received information and detecting anomalies, and a planning device means for determining safety measures based on the anomalies. This makes it possible to integrate multiple types of sensor information to quickly detect anomalies and immediately take optimal safety measures.

[0461] "Information gathering methods" refer to means of obtaining necessary information from various sensors placed within the home.

[0462] An "information processing device" is a device that receives information obtained from a collection device and performs the necessary processing.

[0463] "Analysis device means" refers to a device that analyzes information received from information processing device means and detects abnormalities from the normal state.

[0464] The "planning device means" is a device for determining the optimal safety measures based on the abnormalities detected by the analysis device means.

[0465] A "control device" is a device for physically implementing the safety measures determined by the planning device.

[0466] An "information provision device" is a device for notifying the user of the execution results and the status of anomaly detection of the control device.

[0467] An "environmental control device" is a device used to control lighting and alarms in a specific area when an abnormality is detected.

[0468] A "control device means" is a device that allows a user to remotely control the system using an information terminal.

[0469] A "user terminal" is a terminal used by a user to receive notifications from information provision devices and to monitor and operate the system.

[0470] A "generative AI model" is an artificial intelligence model used in the process of information analysis and anomaly detection, and includes algorithms for making predictions and judgments.

[0471] A "prompt message" is an instruction or question presented by an information-providing device to the user, intended to prompt the user for a response or instruction.

[0472] To implement this invention, it is first necessary to install various sensors in the home. This allows temperature sensors, humidity sensors, sound sensors, cameras, and WiFi sensors to collect environmental data in real time. The information acquired by these sensors is transmitted to a server, which is an information processing device.

[0473] The server executes an analysis device using a generated AI model and analyzes the acquired data in real time. In doing so, it integrates information from multiple sensors to detect patterns that are different from the normal and recognize anomalies. In this process, for example, suspicious movements or sudden temperature changes during the night, when it is normally quiet, may be detected.

[0474] When an anomaly is detected, the server's planning device quickly determines safety measures. Specifically, based on the type and location of the anomaly, it may automatically turn on the lights or activate the security alarm. Furthermore, the environmental control device can lock the digital lock and send an emergency alert as needed.

[0475] Furthermore, the user's smartphone terminal receives immediate notification through an information provision device. The notification includes the type of anomaly, its location, and the countermeasures taken. At this time, prompt messages are used to provide the user with specific instructions, such as "An abnormal movement has been detected. Please check." or "A temperature rise has been detected. Shall we turn off the heater?"

[0476] Users can directly monitor and control the system from their smartphones via a control device. This two-way operation allows users to respond flexibly to unforeseen circumstances. Thus, the present invention enhances home security and enables a quick and appropriate response to abnormal situations.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The server receives data in real time from various sensors installed in the home. Inputs include temperature, humidity, audio, video, and Wi-Fi connection information. This data is temporarily stored by an information processing unit.

[0480] Step 2:

[0481] The server analyzes the collected data using analytical devices. It integrates diverse sensor information received as input and detects anomalies using a generated AI model. This process primarily employs statistical analysis and anomaly detection algorithms. This yields output regarding the type and location of the anomaly.

[0482] Step 3:

[0483] The server determines the optimal security measures based on the analysis results using a planning device. It receives the type and location information of the anomaly output by the AI ​​as input and determines actions such as activating the security alarm, turning on the lights, and locking the digital lock. This results in the output of a specific action plan as a security measure.

[0484] Step 4:

[0485] The terminal physically executes the planned security measures. It receives instructions from the server, operates smart lights, activates alarm systems, and controls digital locks. This ensures that the planned actions are actually carried out.

[0486] Step 5:

[0487] The server notifies the user's terminal of the results of the action and details of the anomaly. The information provision device formats the information regarding the anomaly detection and countermeasures and generates a prompt message. Specifically, a message such as "Anomaly activity has been detected. Please check." is sent. This notification is sent to the user, allowing them to understand the current situation and take additional action.

[0488] Step 6:

[0489] Users can perform further necessary operations via their user terminal. They receive notifications as input and output additional instructions according to prompts. For example, by selecting "off" in response to the prompt "Do you want to turn off the heater?", the user controls the device through the server. This two-way operation allows users to efficiently ensure home security.

[0490] 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.

[0491] To implement this invention, multiple sensors are installed in the home to collect data using WiFi, temperature, humidity, audio, cameras, etc. This data is transmitted in real time to a server by a terminal. The server analyzes the received data and uses an AI algorithm to detect abnormal movements and patterns. Furthermore, this invention incorporates an emotion engine to recognize the user's emotions, determining their emotional state based on audio and video data from the user.

[0492] The emotion information determined by the emotion engine is used as a factor in influencing the server's security action decisions and optimizing responses after anomaly detection. For example, if a user is in a state of tension, the system is adjusted to prompt a faster response than usual. Furthermore, user emotion information is notified in a customized format via notification methods. If it is detected that a user is experiencing stress, a notification can be sent to the user including calming recommendations.

[0493] When an anomaly is detected, the terminal takes appropriate security actions based on commands from the server. For example, if it is detected that a resident is not in a calm state of mind during an anomaly, the terminal's automatic locking and emergency contact functions will be enhanced. The results of these actions are immediately notified to the user, and it is possible to send further instructions to the system based on the results.

[0494] As a specific example, consider a scenario where an unusual sound is detected late at night, and based on video data obtained from a camera inside the house, it is detected that the user is in an unstable emotional state. In this case, in addition to normal security measures, the server immediately notifies selected relatives and security companies and takes all possible safety measures. In this way, the present invention provides more advanced security and safety management through emotional state analysis.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The device collects data from various sensors installed in the home. This includes data on temperature, humidity, sound, and camera footage, and is measured in real time.

[0498] Step 2:

[0499] The device sends the collected data to the server. This data includes user voice and video information for the emotion engine.

[0500] Step 3:

[0501] The server analyzes the received data. Using AI algorithms, it detects patterns that deviate from normal conditions and identifies anomalies.

[0502] Step 4:

[0503] The server analyzes audio and video data using an emotion engine to recognize the user's emotional state. This analysis includes factors such as voice tone and facial expressions.

[0504] Step 5:

[0505] The server plans appropriate security actions based on the type of anomaly and the user's emotional state. It develops flexible responses that take into account the level of risk and the user's emotional state.

[0506] Step 6:

[0507] The terminal will follow instructions from the server and execute planned security actions. If necessary, it will turn on lights, activate security alarms, and lock doors.

[0508] Step 7:

[0509] Users receive notifications based on the analysis results. These notifications include details of the anomaly, the security actions taken, and advice tailored to the user's emotions.

[0510] Step 8:

[0511] Based on the information provided, users can communicate additional instructions to the system via the application. This allows the system to issue instructions tailored to the user's situation if further action is required.

[0512] (Example 2)

[0513] 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."

[0514] In modern home environments, crime prevention and safety management are becoming increasingly important. However, conventional security systems often only perform simple anomaly detection and lack optimization through in-depth analysis of user emotions and behavioral patterns. Furthermore, the uniform notification system to users after anomaly detection may prevent adequate measures from being taken that are appropriate for each household and situation.

[0515] 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.

[0516] In this invention, the server includes means for receiving environmental data and user data from collection means, means for analyzing the received data using an AI algorithm to detect anomalies, and means for optimizing security actions by utilizing the emotion score obtained by the analysis means. This enables not only simple anomaly detection but also flexible and customized security and safety management that responds to the user's emotional state.

[0517] "Collection means" refers to devices or functions that acquire environmental data and user data and transmit them to a server.

[0518] A "server means" is a central control unit or system for storing and analyzing received data.

[0519] "Analysis means" refers to a function that uses AI algorithms to analyze received data and detect anomalies.

[0520] The "planning method" is a function that optimizes crime prevention actions by utilizing emotion scores based on information obtained through analysis methods.

[0521] "Terminal means" refers to devices or systems used to physically carry out planned crime prevention actions.

[0522] A "notification method" is a communication function that informs the user of the results of the terminal device's execution and recommendations.

[0523] An "AI algorithm" is a computational method based on artificial intelligence technology used for data analysis and prediction.

[0524] An "emotion score" is an index used to numerically evaluate a user's emotional state based on audio and video data.

[0525] This invention provides an embodiment of a system that enhances security and user emotional management within the home. Its specific configuration and operation are described below.

[0526] The system collects environmental and user data using multiple sensors. These sensors include temperature sensors, humidity sensors, sound sensors, and cameras. These sensors collect data and transmit it to the device in real time via Wi-Fi.

[0527] The terminal functions as a gateway to receive data from this sensor and send it to the server. This communication method uses the HTTPS protocol to ensure data security.

[0528] The server is the central device that analyzes the received data and uses AI algorithms to detect anomalies. The AI ​​algorithms learn models based on daily life patterns and identify anomalous events. Deep learning techniques can be used in particular for this analysis. Furthermore, the server analyzes audio and video data, measures the user's emotional state through an emotion engine, and generates an emotion score.

[0529] The emotional score influences the planning of security actions. When an anomaly is detected, the server plans the optimal security action while taking into account the acquired emotional information. This process ensures that security actions are not merely a reaction to an anomaly, but are more appropriately adjusted by considering the user's mental state.

[0530] Users receive the results from their device via a notification system. These notifications include a description of the current situation and suggested actions, customized based on the user's emotional state. For example, if the system determines the user is stressed, a notification will be sent containing advice to help them calm down.

[0531] For example, if an unusual sound is detected late at night, the server analyzes the video data from the camera and determines that the user is in an unstable state. In this case, relatives and security companies are immediately notified, and appropriate security measures are taken.

[0532] An example of a prompt to the generating AI model is, "Please suggest the best security action to take when a user is emotionally unstable and an unusual noise is detected late at night." Based on this prompt, the AI ​​generates specific countermeasures to support the overall operation of the system.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] The device collects environmental and user data from sensors installed in the home. Inputs are real-time data obtained from temperature, humidity, sound, and camera sensors. The device then structures this data and prepares it for transmission to a server via Wi-Fi. Outputs are structured JSON data.

[0536] Step 2:

[0537] The terminal sends the collected data to the server. The input is the structured data generated in step 1. The data is securely sent to the server using the HTTPS protocol. The output is the environment data and user data received by the server.

[0538] Step 3:

[0539] The server analyzes the received data. The input is structured data sent from the terminal. It applies an AI algorithm and evaluates the data using deep learning techniques. It compares the data to normal conditions and detects abnormal patterns. The output is result data indicating whether or not an anomaly was detected.

[0540] Step 4:

[0541] The server analyzes the user's emotional state using an emotion engine. Input consists of audio and video data. It generates a user emotion score by combining speech recognition and image analysis technologies. The output is numerical emotion score data.

[0542] Step 5:

[0543] The server plans security actions based on the analysis results. The inputs are anomaly detection results and sentiment scores. An AI model is used to optimize the necessary actions. If the sentiment score is high, rapid response measures are considered. The output is a specific security action plan.

[0544] Step 6:

[0545] The terminal executes security actions based on instructions from the server. The input is the security action plan, which includes actions such as automatically locking doors, activating alarms, and sending emergency notifications to selected contacts. The output is the result of the actions performed.

[0546] Step 7:

[0547] The user receives notifications about the execution results and recommended actions. Input is the processing result from the terminal. The notification means provides the user with an explanation of the anomaly and corresponding suggestions and advice. Output is customized notification information sent to the user.

[0548] (Application Example 2)

[0549] 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."

[0550] In modern society, crime prevention and safety are extremely important issues, but conventional systems do not adequately address the emotional state of users in their living spaces to provide efficient alarm responses. As a result, unnecessary alarms may be triggered, or a quick response may not be possible when truly necessary. Therefore, there is a need for a system that considers the emotional state of users when detecting abnormalities or dangers, and that simultaneously optimizes crime prevention and a sense of security.

[0551] 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.

[0552] In this invention, the server includes an information processing device, an analysis device, and a planning device. This enables the formulation of an action plan that reflects abnormal and emotional states.

[0553] "Data collection means" refers to multiple sensors installed in a home that collect information such as Wi-Fi, temperature, humidity, sound, and camera data.

[0554] "Information processing device means" refers to a device that receives collected data and transmits it to a server.

[0555] "Analysis means" refers to algorithms and programs used to detect anomalies or the emotional state of users using the received data.

[0556] "Planning means" refers to the process of determining the optimal action plan based on the results detected by the analysis means.

[0557] "Terminal device means" refers to a device that performs operations on physical equipment or systems in accordance with a predetermined action plan.

[0558] "Notification device means" refers to a device that provides users with information and recommendations tailored to their actions and emotional state.

[0559] An "anomaly" refers to movements, sounds, or other phenomena that deviate from the normal patterns based on sensor data.

[0560] "Emotional state" refers to the user's psychological state as inferred from voice and camera data.

[0561] The system for implementing this invention consists of multiple sensors installed in the home and a device that collects data from them. This system collects information such as WiFi, temperature, humidity, voice, and camera data, and transmits it to a server in real time via an information processing device. The server analyzes the received data using an AI algorithm to detect anomalies and the user's emotional state. AI frameworks such as TensorFlow and PyTorch are often used for the analysis.

[0562] Based on the analyzed data, the planning system develops an action plan that takes into account abnormalities and emotional states. In particular, if the user is determined to be experiencing tension or an unstable emotional state, the plan is modified to ensure faster and more appropriate action than usual. Once the action plan is determined, the terminal device performs the actual operations, implementing security measures and notifying the user. Notifications are sent to the user's smartphone or other devices using the Twilio API, etc.

[0563] Furthermore, the notification device uses generative AI models such as OpenAI's GPT to generate customized messages tailored to the user's emotional state, providing suggestions and information to alleviate stress.

[0564] For example, if an unusual sound is detected late at night and the camera footage indicates that the user is in an unstable state, the server will immediately and automatically notify selected relatives or security organizations. Additionally, a suggested music playlist for relaxation will be sent to the user's smartphone. An example of a prompt for the generating AI model is, "Generate a message to provide reassurance based on the user's emotional state."

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The server receives data from multiple sensors installed within the home. Inputs include temperature, humidity, audio, and camera footage. This data is integrated in real time and temporarily stored in a database.

[0568] Step 2:

[0569] The server analyzes the received data using AI frameworks such as TensorFlow and PyTorch. The input is the sensor data obtained in step 1, and the server performs data processing to detect anomalies and recognize the type and pattern of the anomalies. The output is information about the presence and type of anomalies.

[0570] Step 3:

[0571] The server uses a generative AI model (such as OpenAI's GPT) based on the analysis results to recognize the user's emotional state. The input consists of the anomaly information from step 2 and sensor data, and the emotion recognition algorithm performs data calculations to estimate the emotional state. The output is information about the user's emotional state.

[0572] Step 4:

[0573] The server formulates an action plan based on the analysis results and the user's emotional state. The input is the output data from steps 2 and 3, and the server processes this data to determine the optimal crime prevention action for the given situation. The output is information related to the action plan.

[0574] Step 5:

[0575] The terminal receives an action plan provided by the server and performs physical or digital actions based on it. The input is the action plan from step 4, and the necessary measures are implemented. The output is the status of the crime prevention actions.

[0576] Step 6:

[0577] The server uses the Twilio API to notify the user of the execution results from the notification device. The input is the status of the crime prevention action performed in step 5, and the data is processed to generate a message according to the emotional state. The output is the content of the notification sent to the user's smartphone.

[0578] 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.

[0579] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0580] 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.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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".

[0595] To implement this invention, multiple sensors installed in the home must collect data in real time. This involves a variety of sensors, including WiFi sensors, temperature sensors, humidity sensors, sound sensors, and cameras. These sensors continuously monitor the conditions within the home and transmit the data to a server at specified time intervals.

[0596] The server is responsible for analyzing the received data. AI algorithms are used for data analysis, recognizing anomalies by detecting patterns that deviate from normal conditions. For example, it recognizes suspicious movement during periods of low activity, sudden temperature changes, or unusual sounds as anomalies.

[0597] When an anomaly is detected, the server plans the optimal countermeasures based on a pre-configured security plan. This includes specific actions to enhance security. The planned actions are executed by various devices located within the home.

[0598] The device performs appropriate security actions based on instructions from the server. This includes automatically turning on lights in areas where an anomaly is detected and activating security alarms. It also ensures family safety by automatically locking digital locks or sending emergency calls depending on the level of risk detected by the anomaly.

[0599] Finally, users can receive detailed information about anomalies in real time through the application. Notifications include the type of anomaly, its location, and the actions taken, allowing users to monitor the situation at home via their smartphone or computer. After receiving a notification, users can also send further instructions to the system as needed, enabling them to respond flexibly to unexpected situations.

[0600] In this way, the present invention achieves highly accurate home security and monitoring through multiple sensors, AI analysis, and automated terminal control.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The device collects data from multiple sensors installed in the home. These include sensors for temperature, humidity, sound, and cameras, and the data is measured at regular intervals.

[0604] Step 2:

[0605] The device sends the collected data to the server. This data includes numerical values ​​and image data based on the timestamp and sensor type.

[0606] Step 3:

[0607] The server analyzes the received data and compares it to normal patterns using an AI algorithm. This identifies data points that are considered anomaly.

[0608] Step 4:

[0609] When an anomaly is detected, the server determines the optimal security action based on a pre-configured security plan. The planned measures include risk assessment.

[0610] Step 5:

[0611] The terminal receives instructions from the server and executes planned security actions. These actions include specific measures such as turning on lights, activating security alarms, and locking digital locks.

[0612] Step 6:

[0613] Users receive notifications of anomaly detection through the application. The notifications include details of the location, nature of the anomaly, and the actions taken.

[0614] Step 7:

[0615] Users can use the application to send additional instructions to the system, enabling flexible responses to changing situations.

[0616] (Example 1)

[0617] 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".

[0618] In today's world, ensuring the safety of homes and facilities with high accuracy requires improving the efficiency and reliability of anomaly detection systems. Conventional systems can miss anomalies or, conversely, misidentify them, which reduces the effectiveness of security measures. Therefore, there is a need to develop systems that can detect anomalies more accurately and enable swift and appropriate countermeasures.

[0619] 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.

[0620] In this invention, the server includes a processing unit that receives information from a data collection device, an algorithm device that performs advanced analysis to recognize abnormal conditions, and a planning device that formulates countermeasures based on the abnormal conditions. This enables the integration of data from various sensors and the rapid and accurate detection of abnormal conditions.

[0621] A "data collection device" is a device that collects diverse information from the environment using sensors and other means.

[0622] A "processing device" is a device that receives information obtained from a collection device and processes it appropriately.

[0623] An "algorithmic device" is a device that highly analyzes received information and accurately recognizes abnormal conditions.

[0624] A "planning device" is a device that formulates the optimal countermeasures based on recognized abnormal conditions.

[0625] A "control device" is a device used to actually implement the countermeasures that have been formulated.

[0626] A "communication device" is a device used to report the details and status of operations performed by the control device to the user and to transmit necessary information.

[0627] To implement this invention, various sensors installed in homes or facilities are required. These sensors include a variety of devices such as WiFi-enabled communication sensors, temperature sensors for measuring temperature, humidity sensors for measuring humidity, sound sensors for capturing sound, and cameras for capturing visual information. These sensors play a role in monitoring various environmental data in real time and providing information to data collection devices.

[0628] The server receives data transmitted from the collection device and manages it appropriately using a processing unit. This processing utilizes an advanced algorithmic device employing a generative AI model. The algorithmic device is a key component for quickly recognizing anomalies from normal environmental conditions. This analysis makes it possible to detect unexpected events such as suspicious movements or sudden temperature changes.

[0629] When an anomaly is detected, the server, via a planning device, formulates the optimal response based on a pre-configured list of countermeasures. For example, in the area where the anomaly is detected, the control device may automatically turn on the lights or activate a security alarm.

[0630] Furthermore, users can receive real-time notifications via communication devices about abnormal conditions and the measures taken. If a dedicated application is installed on the user's terminal, they can immediately view the notifications and check the status of their home or facility. This allows users to respond flexibly to the situation.

[0631] For example, if a user enters a prompt message into the system such as "Instruct the system to strengthen security measures while I'm out," the server can automatically take immediate action, such as raising the security level and strengthening anomaly monitoring.

[0632] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0633] Step 1:

[0634] The server receives data from various sensors within the home. Specifically, it collects temperature information from temperature sensors, humidity data from humidity sensors, sound levels from sound sensors, and video data from cameras. This input data is integrated by the server's processing unit and converted into a format that can be accessed in real time.

[0635] Step 2:

[0636] The received data is analyzed by an algorithmic device on the server. Using a generative AI model, it performs analysis to distinguish between normal and abnormal patterns. For example, it can detect a sudden temperature increase outside the normal temperature range or suspicious noises at night. If an abnormal condition is recognized based on the input data, it outputs a flag indicating the abnormality.

[0637] Step 3:

[0638] If an anomaly flag is raised, the server's planning device formulates security measures. Specifically, it determines countermeasures such as turning on lights or activating alarms based on the information obtained from sensors and the type of anomaly, and outputs the details of these measures.

[0639] Step 4:

[0640] Based on the formulated countermeasures, the terminals execute them through the control unit. For example, if an anomaly is detected, the lights in the designated room will automatically turn on, or a security alarm will sound. This implements a physical security measure.

[0641] Step 5:

[0642] The results of security measures are notified to users in real time via communication devices. Specifically, the actions taken and the current situation are reported via push notifications to the user's terminal or email. Upon receiving the notification, the user can send further instructions to the system as input and adjust the system's operation as needed.

[0643] (Application Example 1)

[0644] 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".

[0645] In recent years, despite the increasing importance of home security, conventional security systems have limitations in real-time situation monitoring and immediate response. It is difficult for users to quickly take necessary measures after detecting an anomaly, and insufficient responses contribute to security vulnerabilities. Furthermore, conventional systems require the effective integration of diverse sensor information and accurate recognition of anomaly patterns. Against this backdrop, there is a need for a system that ensures a high level of home security while reducing the burden on users.

[0646] 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.

[0647] In this invention, the server includes an information processing device means for receiving information from a collection means, an analysis device means for analyzing the received information and detecting anomalies, and a planning device means for determining safety measures based on the anomalies. This makes it possible to integrate multiple types of sensor information to quickly detect anomalies and immediately take optimal safety measures.

[0648] "Information gathering methods" refer to means of obtaining necessary information from various sensors placed within the home.

[0649] An "information processing device" is a device that receives information obtained from a collection device and performs the necessary processing.

[0650] "Analysis device means" refers to a device that analyzes information received from information processing device means and detects abnormalities from the normal state.

[0651] The "planning device means" is a device for determining the optimal safety measures based on the abnormalities detected by the analysis device means.

[0652] A "control device" is a device for physically implementing the safety measures determined by the planning device.

[0653] An "information provision device" is a device for notifying the user of the execution results and the status of anomaly detection of the control device.

[0654] An "environmental control device" is a device used to control lighting and alarms in a specific area when an abnormality is detected.

[0655] A "control device means" is a device that allows a user to remotely control the system using an information terminal.

[0656] A "user terminal" is a terminal used by a user to receive notifications from information provision devices and to monitor and operate the system.

[0657] A "generative AI model" is an artificial intelligence model used in the process of information analysis and anomaly detection, and includes algorithms for making predictions and judgments.

[0658] A "prompt message" is an instruction or question presented by an information-providing device to the user, intended to prompt the user for a response or instruction.

[0659] To implement this invention, it is first necessary to install various sensors in the home. This allows temperature sensors, humidity sensors, sound sensors, cameras, and WiFi sensors to collect environmental data in real time. The information acquired by these sensors is transmitted to a server, which is an information processing device.

[0660] The server executes an analysis device using a generated AI model and analyzes the acquired data in real time. In doing so, it integrates information from multiple sensors to detect patterns that are different from the normal and recognize anomalies. In this process, for example, suspicious movements or sudden temperature changes during the night, when it is normally quiet, may be detected.

[0661] When an anomaly is detected, the server's planning device quickly determines safety measures. Specifically, based on the type and location of the anomaly, it may automatically turn on the lights or activate the security alarm. Furthermore, the environmental control device can lock the digital lock and send an emergency alert as needed.

[0662] Furthermore, the user's smartphone terminal receives immediate notification through an information provision device. The notification includes the type of anomaly, its location, and the countermeasures taken. At this time, prompt messages are used to provide the user with specific instructions, such as "An abnormal movement has been detected. Please check." or "A temperature rise has been detected. Shall we turn off the heater?"

[0663] Users can directly monitor and control the system from their smartphones via a control device. This two-way operation allows users to respond flexibly to unforeseen circumstances. Thus, the present invention enhances home security and enables a quick and appropriate response to abnormal situations.

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] The server receives data in real time from various sensors installed in the home. Inputs include temperature, humidity, audio, video, and Wi-Fi connection information. This data is temporarily stored by an information processing unit.

[0667] Step 2:

[0668] The server analyzes the collected data using analytical devices. It integrates diverse sensor information received as input and detects anomalies using a generated AI model. This process primarily employs statistical analysis and anomaly detection algorithms. This yields output regarding the type and location of the anomaly.

[0669] Step 3:

[0670] The server determines the optimal security measures based on the analysis results using a planning device. It receives the type and location information of the anomaly output by the AI ​​as input and determines actions such as activating the security alarm, turning on the lights, and locking the digital lock. This results in the output of a specific action plan as a security measure.

[0671] Step 4:

[0672] The terminal physically executes the planned security measures. It receives instructions from the server, operates smart lights, activates alarm systems, and controls digital locks. This ensures that the planned actions are actually carried out.

[0673] Step 5:

[0674] The server notifies the user's terminal of the results of the action and details of the anomaly. The information provision device formats the information regarding the anomaly detection and countermeasures and generates a prompt message. Specifically, a message such as "Anomaly activity has been detected. Please check." is sent. This notification is sent to the user, allowing them to understand the current situation and take additional action.

[0675] Step 6:

[0676] Users can perform further necessary operations via their user terminal. They receive notifications as input and output additional instructions according to prompts. For example, by selecting "off" in response to the prompt "Do you want to turn off the heater?", the user controls the device through the server. This two-way operation allows users to efficiently ensure home security.

[0677] 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.

[0678] To implement this invention, multiple sensors are installed in the home to collect data using WiFi, temperature, humidity, audio, cameras, etc. This data is transmitted in real time to a server by a terminal. The server analyzes the received data and uses an AI algorithm to detect abnormal movements and patterns. Furthermore, this invention incorporates an emotion engine to recognize the user's emotions, determining their emotional state based on audio and video data from the user.

[0679] The emotion information determined by the emotion engine is used as a factor in influencing the server's security action decisions and optimizing responses after anomaly detection. For example, if a user is in a state of tension, the system is adjusted to prompt a faster response than usual. Furthermore, user emotion information is notified in a customized format via notification methods. If it is detected that a user is experiencing stress, a notification can be sent to the user including calming recommendations.

[0680] When an anomaly is detected, the terminal takes appropriate security actions based on commands from the server. For example, if it is detected that a resident is not in a calm state of mind during an anomaly, the terminal's automatic locking and emergency contact functions will be enhanced. The results of these actions are immediately notified to the user, and it is possible to send further instructions to the system based on the results.

[0681] As a specific example, consider a scenario where an unusual sound is detected late at night, and based on video data obtained from a camera inside the house, it is detected that the user is in an unstable emotional state. In this case, in addition to normal security measures, the server immediately notifies selected relatives and security companies and takes all possible safety measures. In this way, the present invention provides more advanced security and safety management through emotional state analysis.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The device collects data from various sensors installed in the home. This includes data on temperature, humidity, sound, and camera footage, and is measured in real time.

[0685] Step 2:

[0686] The device sends the collected data to the server. This data includes user voice and video information for the emotion engine.

[0687] Step 3:

[0688] The server analyzes the received data. Using AI algorithms, it detects patterns that deviate from normal conditions and identifies anomalies.

[0689] Step 4:

[0690] The server analyzes audio and video data using an emotion engine to recognize the user's emotional state. This analysis includes factors such as voice tone and facial expressions.

[0691] Step 5:

[0692] The server plans appropriate security actions based on the type of anomaly and the user's emotional state. It develops flexible responses that take into account the level of risk and the user's emotional state.

[0693] Step 6:

[0694] The terminal will follow instructions from the server and execute planned security actions. If necessary, it will turn on lights, activate security alarms, and lock doors.

[0695] Step 7:

[0696] Users receive notifications based on the analysis results. These notifications include details of the anomaly, the security actions taken, and advice tailored to the user's emotions.

[0697] Step 8:

[0698] Based on the information provided, users can communicate additional instructions to the system via the application. This allows the system to issue instructions tailored to the user's situation if further action is required.

[0699] (Example 2)

[0700] 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".

[0701] In modern home environments, crime prevention and safety management are becoming increasingly important. However, conventional security systems often only perform simple anomaly detection and lack optimization through in-depth analysis of user emotions and behavioral patterns. Furthermore, the uniform notification system to users after anomaly detection may prevent adequate measures from being taken that are appropriate for each household and situation.

[0702] 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.

[0703] In this invention, the server includes means for receiving environmental data and user data from collection means, means for analyzing the received data using an AI algorithm to detect anomalies, and means for optimizing security actions by utilizing the emotion score obtained by the analysis means. This enables not only simple anomaly detection but also flexible and customized security and safety management that responds to the user's emotional state.

[0704] "Collection means" refers to devices or functions that acquire environmental data and user data and transmit them to a server.

[0705] A "server means" is a central control unit or system for storing and analyzing received data.

[0706] "Analysis means" refers to a function that uses AI algorithms to analyze received data and detect anomalies.

[0707] The "planning method" is a function that optimizes crime prevention actions by utilizing emotion scores based on information obtained through analysis methods.

[0708] "Terminal means" refers to devices or systems used to physically carry out planned crime prevention actions.

[0709] A "notification method" is a communication function that informs the user of the results of the terminal device's execution and recommendations.

[0710] An "AI algorithm" is a computational method based on artificial intelligence technology used for data analysis and prediction.

[0711] An "emotion score" is an index used to numerically evaluate a user's emotional state based on audio and video data.

[0712] This invention provides an embodiment of a system that enhances security and user emotional management within the home. Its specific configuration and operation are described below.

[0713] The system collects environmental and user data using multiple sensors. These sensors include temperature sensors, humidity sensors, sound sensors, and cameras. These sensors collect data and transmit it to the device in real time via Wi-Fi.

[0714] The terminal functions as a gateway to receive data from this sensor and send it to the server. This communication method uses the HTTPS protocol to ensure data security.

[0715] The server is the central device that analyzes the received data and uses AI algorithms to detect anomalies. The AI ​​algorithms learn models based on daily life patterns and identify anomalous events. Deep learning techniques can be used in particular for this analysis. Furthermore, the server analyzes audio and video data, measures the user's emotional state through an emotion engine, and generates an emotion score.

[0716] The emotional score influences the planning of security actions. When an anomaly is detected, the server plans the optimal security action while taking into account the acquired emotional information. This process ensures that security actions are not merely a reaction to an anomaly, but are more appropriately adjusted by considering the user's mental state.

[0717] Users receive the results from their device via a notification system. These notifications include a description of the current situation and suggested actions, customized based on the user's emotional state. For example, if the system determines the user is stressed, a notification will be sent containing advice to help them calm down.

[0718] For example, if an unusual sound is detected late at night, the server analyzes the video data from the camera and determines that the user is in an unstable state. In this case, relatives and security companies are immediately notified, and appropriate security measures are taken.

[0719] An example of a prompt to the generating AI model is, "Please suggest the best security action to take when a user is emotionally unstable and an unusual noise is detected late at night." Based on this prompt, the AI ​​generates specific countermeasures to support the overall operation of the system.

[0720] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0721] Step 1:

[0722] The device collects environmental and user data from sensors installed in the home. Inputs are real-time data obtained from temperature, humidity, sound, and camera sensors. The device then structures this data and prepares it for transmission to a server via Wi-Fi. Outputs are structured JSON data.

[0723] Step 2:

[0724] The terminal sends the collected data to the server. The input is the structured data generated in step 1. The data is securely sent to the server using the HTTPS protocol. The output is the environment data and user data received by the server.

[0725] Step 3:

[0726] The server analyzes the received data. The input is structured data sent from the terminal. It applies an AI algorithm and evaluates the data using deep learning techniques. It compares the data to normal conditions and detects abnormal patterns. The output is result data indicating whether or not an anomaly was detected.

[0727] Step 4:

[0728] The server analyzes the user's emotional state using an emotion engine. Input consists of audio and video data. It generates a user emotion score by combining speech recognition and image analysis technologies. The output is numerical emotion score data.

[0729] Step 5:

[0730] The server plans security actions based on the analysis results. The inputs are anomaly detection results and sentiment scores. An AI model is used to optimize the necessary actions. If the sentiment score is high, rapid response measures are considered. The output is a specific security action plan.

[0731] Step 6:

[0732] The terminal executes security actions based on instructions from the server. The input is the security action plan, which includes actions such as automatically locking doors, activating alarms, and sending emergency notifications to selected contacts. The output is the result of the actions performed.

[0733] Step 7:

[0734] The user receives notifications about the execution results and recommended actions. Input is the processing result from the terminal. The notification means provides the user with an explanation of the anomaly and corresponding suggestions and advice. Output is customized notification information sent to the user.

[0735] (Application Example 2)

[0736] 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".

[0737] In modern society, crime prevention and safety are extremely important issues, but conventional systems do not adequately address the emotional state of users in their living spaces to provide efficient alarm responses. As a result, unnecessary alarms may be triggered, or a quick response may not be possible when truly necessary. Therefore, there is a need for a system that considers the emotional state of users when detecting abnormalities or dangers, and that simultaneously optimizes crime prevention and a sense of security.

[0738] 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.

[0739] In this invention, the server includes an information processing device, an analysis device, and a planning device. This enables the formulation of an action plan that reflects abnormal and emotional states.

[0740] "Data collection means" refers to multiple sensors installed in a home that collect information such as Wi-Fi, temperature, humidity, sound, and camera data.

[0741] "Information processing device means" refers to a device that receives collected data and transmits it to a server.

[0742] "Analysis means" refers to algorithms and programs used to detect anomalies or the emotional state of users using the received data.

[0743] "Planning means" refers to the process of determining the optimal action plan based on the results detected by the analysis means.

[0744] "Terminal device means" refers to a device that performs operations on physical equipment or systems in accordance with a predetermined action plan.

[0745] "Notification device means" refers to a device that provides users with information and recommendations tailored to their actions and emotional state.

[0746] An "anomaly" refers to movements, sounds, or other phenomena that deviate from the normal patterns based on sensor data.

[0747] "Emotional state" refers to the user's psychological state as inferred from voice and camera data.

[0748] The system for implementing this invention consists of multiple sensors installed in the home and a device that collects data from them. This system collects information such as WiFi, temperature, humidity, voice, and camera data, and transmits it to a server in real time via an information processing device. The server analyzes the received data using an AI algorithm to detect anomalies and the user's emotional state. AI frameworks such as TensorFlow and PyTorch are often used for the analysis.

[0749] Based on the analyzed data, the planning system develops an action plan that takes into account abnormalities and emotional states. In particular, if the user is determined to be experiencing tension or an unstable emotional state, the plan is modified to ensure faster and more appropriate action than usual. Once the action plan is determined, the terminal device performs the actual operations, implementing security measures and notifying the user. Notifications are sent to the user's smartphone or other devices using the Twilio API, etc.

[0750] Furthermore, the notification device uses generative AI models such as OpenAI's GPT to generate customized messages tailored to the user's emotional state, providing suggestions and information to alleviate stress.

[0751] For example, if an unusual sound is detected late at night and the camera footage indicates that the user is in an unstable state, the server will immediately and automatically notify selected relatives or security organizations. Additionally, a suggested music playlist for relaxation will be sent to the user's smartphone. An example of a prompt for the generating AI model is, "Generate a message to provide reassurance based on the user's emotional state."

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The server receives data from multiple sensors installed within the home. Inputs include temperature, humidity, audio, and camera footage. This data is integrated in real time and temporarily stored in a database.

[0755] Step 2:

[0756] The server analyzes the received data using AI frameworks such as TensorFlow and PyTorch. The input is the sensor data obtained in step 1, and the server performs data processing to detect anomalies and recognize the type and pattern of the anomalies. The output is information about the presence and type of anomalies.

[0757] Step 3:

[0758] The server uses a generative AI model (such as OpenAI's GPT) based on the analysis results to recognize the user's emotional state. The input consists of the anomaly information from step 2 and sensor data, and the emotion recognition algorithm performs data calculations to estimate the emotional state. The output is information about the user's emotional state.

[0759] Step 4:

[0760] The server formulates an action plan based on the analysis results and the user's emotional state. The input is the output data from steps 2 and 3, and the server processes this data to determine the optimal crime prevention action for the given situation. The output is information related to the action plan.

[0761] Step 5:

[0762] The terminal receives an action plan provided by the server and performs physical or digital actions based on it. The input is the action plan from step 4, and the necessary measures are implemented. The output is the status of the crime prevention actions.

[0763] Step 6:

[0764] The server uses the Twilio API to notify the user of the execution results from the notification device. The input is the status of the crime prevention action performed in step 5, and the data is processed to generate a message according to the emotional state. The output is the content of the notification sent to the user's smartphone.

[0765] 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.

[0766] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0767] In the above embodiment, an example was given in which the 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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."

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] The following is further disclosed regarding the embodiments described above.

[0787] (Claim 1)

[0788] A server means that receives data from the collection means,

[0789] An analysis means for analyzing the received data and detecting anomalies,

[0790] A planning means for determining crime prevention actions based on the aforementioned abnormality,

[0791] A terminal means for executing the aforementioned determined crime prevention action,

[0792] A notification means for notifying the user of the execution result of the terminal means,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the analysis means integrates multiple types of data from the sensor to detect anomalies.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein the notification means provides information to the user terminal in real time.

[0798] "Example 1"

[0799] (Claim 1)

[0800] A processing unit that receives information from a collection device,

[0801] An algorithm device that performs advanced analysis of the received information to recognize an abnormal state,

[0802] A planning device that formulates countermeasures based on the recognized abnormal state,

[0803] A control device for implementing the aforementioned formulated countermeasures,

[0804] A communication device that reports the details of the actions taken by the control device to the user,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the algorithm device integrates multiple types of information from detectors to recognize an abnormal state.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the communication device provides information to the user terminal immediately.

[0810] "Application Example 1"

[0811] (Claim 1)

[0812] Information processing means for receiving information from collection means,

[0813] An analysis device means for analyzing the received information and detecting anomalies,

[0814] A planning device means for determining safety measures based on the aforementioned abnormality,

[0815] A control device means for executing the safety measures determined above,

[0816] Information provision device means for notifying the user of the execution result of the control device means,

[0817] An environmental control device that controls lighting and alarms when an anomaly is detected,

[0818] A control device means that can be remotely controlled by the user using an information terminal,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the analysis device means integrates multiple types of information from the sensing device to detect an anomaly, and the control device means provides instructions using a generated AI model.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the information providing device means immediately provides information to the user terminal and prompts the user to respond using a prompt message.

[0824] "Example 2 of combining an emotion engine"

[0825] (Claim 1)

[0826] A server means that receives environmental data and user data from a collection means,

[0827] An analysis means that analyzes the received data using an AI algorithm to detect anomalies,

[0828] A planning means for optimizing crime prevention actions using the emotion score obtained by the analysis means,

[0829] A terminal means for performing the optimized security action,

[0830] A notification means that provides the user with the execution results and recommendations of the terminal means,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the analysis means integrates multiple types of data from sensors and determines the user's emotional state using an emotion engine.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the notification means provides real-time notifications to the user terminal with customized content.

[0836] "Application example 2 when combining with an emotional engine"

[0837] (Claim 1)

[0838] Information processing device means that receives data from collection means,

[0839] An analysis means for analyzing the received data and detecting abnormalities and the emotional state of the user,

[0840] A planning means for formulating an action plan based on the aforementioned abnormalities and emotional states,

[0841] A terminal device means that executes the aforementioned determined action plan and operates a predetermined device,

[0842] A notification device means that notifies the user of the execution results of the terminal device means and provides recommendations according to the user's emotional state,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the analysis means integrates multiple types of information from multiple detectors to detect abnormalities and emotional states.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the notification device means provides information to the user terminal in real time and generates personalized messages according to the emotional state. [Explanation of symbols]

[0848] 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 server means that receives data from the collection means, An analysis means for analyzing the received data and detecting anomalies, A planning means for determining crime prevention actions based on the aforementioned abnormality, A terminal means for executing the aforementioned determined crime prevention action, A notification means for notifying the user of the execution result of the terminal means, A system that includes this.

2. The system according to claim 1, wherein the analysis means integrates multiple types of data from the sensor to detect anomalies.

3. The system according to claim 1, wherein the notification means provides information to the user terminal in real time.

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